Plot method for
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A sienaTimeTest object returned by
A Boolean value corresponding to whether the user
would like a pairwise plot of the simulated statistics to assess
correlation among the effects (
A vector of integers corresponding to the indices given
A positive number corresponding to the number of standard deviations on one step estimates to use for computing the maximum and minimum of the plotting range. We recommend experimenting with this number when the y-axes of the plots are not satisfactory. Smaller numbers shrink the axes.
A list of three decimals indicating the gradients at which to draw the confidence interval bars.
For extra arguments. The Lattice parameter
pairwise=TRUE plot may be used to assess whether effects are
highly correlated. This information may be important when considering
forward-model selection, since highly correlated effects may have
highly correlated one-step estimates, particularly since the individual
score type tests are not orthogonalized against the scores and
deviations of yet-unestimated dummies. For example, reciprocity and
outdegree may have highly correlated statistics as indicated by a
strong, positive correlation coefficient. When considering whether to
include dummy terms, it may be a good idea to include, e.g., outdegree,
estimate the parameter, and see whether reciprocity dummies remain
significant after method of moments estimation of the updated model–as
opposed to including both outdegree and reciprocity.
pairwise=FALSE plot displays the most of the information
sienaTimeTest in a graphical fashion. For each
effect, the method of moments parameter estimate for the base period
(i.e. wave 1) is given as a blue, horizontal reference line. One step
estimates are given for all of the parameters by dots at each wave. The
dots are colored black if the parameter has been included in the model
already (i.e. has been estimated via method of moments), or red if they
have not been included. Confidence intervals are given based on pivots
pvalues. Evidence of time heterogeneity is suggested by
points with confidence intervals not overlapping with the base period.
See http://www.stats.ox.ac.uk/~snijders/siena/ for general information on RSiena.
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## Not run: myalgorithm <- sienaAlgorithmCreate(nsub=4, n3=500) mynet1 <- sienaDependent(array(c(s501, s502, s503, s501, s503, s502), dim=c(50, 50, 6))) mydata <- sienaDataCreate(mynet1) myeff <- getEffects(mydata) myeff <- includeEffects(myeff, transTrip, balance) myeff <- includeTimeDummy(myeff, recip, timeDummy="2,3,5") myeff <- includeTimeDummy(myeff, balance, timeDummy="4") myeff <- includeTimeDummy(myeff, density, timeDummy="all") ansp <- siena07(myalgorithm, data=mydata, effects=myeff, batch=TRUE) ttp <- sienaTimeTest(ansp) ## Pairwise plots show plot(ttp, pairwise=TRUE) ## Time test plots show plot(ttp, effects=1:3) ## default layout plot(ttp, effects=1:3, layout=c(3,1)) ## End(Not run)
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